Educational Technology and Change Journal

By Jim Shimabukuro (assisted by Perplexity)
Editor

The field of AI is heading into December 2025 with three urgent decisions: how to govern frontier AI models, how to handle the open‑source versus closed‑source race, and how to expand AI compute without blowing through energy, water, and climate constraints. Each of these comes with big power struggles between governments and tech companies, and the choices made in the next few weeks will shape who leads AI, how safe it is, and who gets access.anecdotes+2​

Decision 1: How hard should governments clamp down on frontier AI models?

The first pressing decision is: In December 2025, how far should governments go in imposing binding safety, reporting, and liability rules on the latest ‘frontier’ AI models without crushing innovation? This is urgent because multiple jurisdictions have just moved from abstract principles to real, enforceable rules, and now have to decide where to draw the line on risk thresholds, reporting duties, and penalties.goodwinlaw+2​

Several big moves set the stage. In California, SB 53 (the Transparency in Frontier Artificial Intelligence Act) has created the first dedicated state‑level regime specifically focused on catastrophic‑risk frontier models, forcing large developers to create and maintain a detailed “frontier AI framework” and submit regular risk summaries to emergency authorities. At roughly the same time, the European Union has finalized its AI Act, which uses a tiered risk approach and bans some “unacceptable risk” uses, while placing heavy compliance loads on “high‑risk” systems in areas like law enforcement, employment, and critical infrastructure. A November 2025 global survey of AI regulations emphasizes that Europe, the US, the UK, Japan, and China are all converging on safety, transparency, and accountability as core themes, but with wildly different implementation styles and political goals.digital-strategy.europa+3​

Why December 2025? There are two time pressures. First, regulators now need to translate high‑level principles into practical thresholds: what counts as a “frontier” model, what capability indicators actually trigger SB 53‑style requirements, and how to align state, national, and international rules. A detailed legal analysis of SB 53 from mid‑November notes that California’s Department of Technology and emergency services office must define thresholds and reporting practices that mesh with national and international standards, and then keep revising them as capabilities change. Second, the White House is weighing an executive order that could preempt or undermine some state AI laws, which means the administration has to decide quickly if it wants a stronger federal hand or a looser, sector‑based patchwork.globalpolicywatch+1​

The stakes are huge for the main players. On the government side, the key actors are:

• United States: The Trump White House, the Department of Commerce, the Federal Trade Commission, and a growing cluster of sector regulators pushing AI rules into finance, healthcare, and child‑safety domains.nasdaq+2​
• California: Governor Gavin Newsom, state legislators behind SB 53, and agencies such as the California Office of Emergency Services and Department of Technology.gov.ca+1​
• European Union: The European Commission and national regulators implementing the AI Act.anecdotes+1​
• United Kingdom, Japan, and others: National regulators implementing lighter, “pro‑innovation” or sector‑based regimes.anecdotes​

On the corporate side, the big model developers—OpenAI, Google DeepMind, Anthropic, Meta, xAI, Mistral, leading Chinese labs like Moonshot AI and DeepSeek, and large platforms like Microsoft, Amazon, and Oracle—must decide how to adapt global product and deployment strategies to avoid ending up with a fragmented, unmanageable patchwork of obligations. For frontier labs, SB 53‑style rules mean more internal risk assessment infrastructure, regular catastrophic‑risk reporting, and strong governance over model weights and incident response.clarifai+3​

The decision is so critical because it will define the default safety culture of AI. If regulators undershoot, a handful of firms can deploy very powerful, hard‑to‑predict systems with limited external scrutiny, raising the odds of serious misuse (cyberattacks, bio‑risk, critical infrastructure disruption) and eroding public trust. If they overshoot, especially in a way that is incompatible across regions, they risk driving development into less regulated jurisdictions, entrenching incumbents that can absorb compliance costs, and slowing down beneficial applications in health, education, and climate. A November 2025 international safety update stresses that the acceleration of agentic, tool‑using frontier models since early 2025 has worsened “tail‑risk” scenarios much faster than most governance frameworks anticipated, which heightens the urgency of robust but agile safety rules.internationalaisafetyreport+3​

December 2025 decisions around thresholds, preemption, and cross‑border coordination could set the template for the next decade. If California, the EU, and the US federal government manage to align on core concepts—what counts as catastrophic risk, what incident reporting looks like, and how to handle cross‑border deployment—then even non‑Western jurisdictions will be pressured to harmonize or risk being locked out of global AI supply chains. If they fracture, companies may start shipping different model variants per region, reduce transparency to avoid extra obligations, or strategically base risky work in lightly regulated environments. Either way, these choices in December will heavily influence which political systems end up setting the “constitution” for AI.goodwinlaw+2​

For full citation details on this decision, useful November‑2025 sources include: a Goodwin law client alert on California SB 53 (Goodwin Procter LLP, “California Moves to Regulate Frontier AI With a Focus on Catastrophic Risk,” Goodwin, Nov. 16, 2025, https://www.goodwinlaw.com/…). You can also draw on global overviews such as anecdotes.ai’s “AI Regulations in 2025: US, EU, UK, Japan, China & More” (anecdotes, Nov. 23, 2025, https://www.anecdotes.ai/learn/ai-regulations-in-2025-us-eu-uk-japan-china-and-more) and the European Commission’s AI Act page updated on November 19, 2025 (European Commission, “AI Act,” Shaping Europe’s Digital Future, Nov. 19, 2025, https://digital-strategy.ec.europa.eu/…).[5]goodwinlaw+1​

Decision 2: How open should the frontier of AI be?

The second big question is: In December 2025, should the most capable AI systems trend toward open‑weight/open‑source releases or stay tightly closed, and what guardrails should apply to open models? The answer matters because the technical frontier has shifted dramatically in 2025, with open models now challenging or surpassing closed systems on key benchmarks, which forces governments and companies to decide whether openness is an asset (for innovation and democracy) or a security risk.shakudo+3​

Throughout 2025, a wave of powerful open‑weight models has landed. Meta has been pushing successive Llama releases with strong coding, reasoning, and multilingual performance. Chinese labs like DeepSeek and Moonshot AI have released large mixture‑of‑experts models such as DeepSeek‑V3/R1 and Kimi K2, some under permissive licenses that allow unrestricted commercial use. Mistral has continued to publish performant open models (e.g., Mixtral 8x22B) under Apache‑style terms. By late 2025, technical comparisons show that open models like Kimi K2, Llama 4 variants, and DeepSeek V3 can outperform or closely match proprietary leaders on many non‑specialized tasks, at a fraction of the cost and with much more deployment flexibility. A mid‑November 2025 “Top 9 LLMs” roundup highlights that some open‑weight models now outrun closed offerings such as GPT‑4o or Gemini 2.0 Flash on coding and reasoning benchmarks, especially when fine‑tuned for specific domains.news.smol+2​

At the same time, there is growing political and civil‑liberties attention on the open vs. closed debate. A late‑2025 ACLU piece on “Open vs. Closed” frames the battle as a fight over future digital freedom: whether a few corporations and governments control the most powerful models or whether a diverse ecosystem of open models remains viable. IBM’s November 16, 2025 article on “The open‑source breakthrough shifting AI’s center of gravity” underscores that breakthroughs like Kimi K2 Thinking and other high‑IQ open models have shifted expectations about where cutting‑edge innovation happens, challenging the assumption that only closed labs can produce frontier systems.ibm+1​

The decision is especially pressing in December because governments are simultaneously tightening safety rules and revisiting export controls, and how they treat open models will heavily influence the competitive landscape. If regulators decide that frontier‑level open models are inherently too risky, they might require licenses, restrict certain capability levels from being released openly, or impose stringent obligations on open‑weight distribution platforms. That could protect against misuse—e.g., open models fine‑tuned for cyber‑offense or bio‑threat design—but also risks locking power into a handful of US and Chinese giants that can secure closed systems behind APIs.aclu+4​

On the corporate side, major players have divergent incentives:

• OpenAI and Google/DeepMind: Primarily proprietary, but experimenting with limited open‑weight releases; they have strong commercial reasons to keep the most powerful models closed.shakudo+1​
• Meta, Mistral, DeepSeek, Moonshot AI, Qwen, and others: Leaning harder into open or open‑weight releases as a way to build ecosystems, gain mindshare, and challenge incumbents.clarifai+2​
• Infrastructure platforms like Clarifai, cloud providers, and on‑prem vendors: Betting that organizations will want a mix of open and closed models for cost, latency, and control reasons, and thus are building tooling that treats powerful open models as first‑class citizens.clarifai​

Internationally, this decision intersects with economic development and national security. Open models lower barriers for smaller countries, universities, and startups to build competitive AI systems without massive capital outlays, which could democratize access and spur local innovation. But security agencies worry that unfettered open access to frontier‑level capabilities could accelerate development of offensive cyber tools, disinformation engines, or even enable bio‑weapons design, especially when paired with open datasets.internationalaisafetyreport+4​

How December decisions go—especially around export controls, frontier safety thresholds, and any attempt to differentiate open vs. closed models in regulation—will shape whether open AI continues to be a serious competitor or gets squeezed into a lower‑tier niche. A world where open models remain at or near the frontier likely leads to:

• Faster diffusion of AI capabilities into education, small businesses, and the Global South.
• More robust scientific scrutiny, because models can be inspected and fine‑tuned by independent researchers.
• Harder security problems, because powerful tools are more broadly accessible.

A world where frontier capabilities become heavily gated behind a few corporate APIs likely leads to:

• Stronger central levers for safety and content controls.
• Greater dependence of universities, small labs, and poorer states on a handful of vendors.
• Political fights over AI monopolies and the geopolitics of “AI sovereignty.”

For more information, rely on sources such as Shakudo’s “Top 9 Large Language Models as of November 2025” (Shakudo, Oct. 4, 2025, https://www.shakudo.io/blog/top-9-large-language-models); Clarifai’s analysis “Kimi K2 vs DeepSeek‑V3/R1” (Clarifai, Nov. 17, 2025, https://www.clarifai.com/blog/kimi-k2-vs-deepseek-v3-r1); IBM’s “The open‑source breakthrough shifting AI’s center of gravity” (IBM Think, Nov. 16, 2025, https://www.ibm.com/think/news/open-source-models-surpass-closed-models); and the ACLU’s “Open vs. Closed: The Battle for the Future of Language Models” (ACLU, Oct. 16, 2025, https://www.aclu.org/news/privacy-technology/open-source-llms).[12]aclu+2​

Decision 3: How aggressively should the world expand AI compute given energy, water, and climate limits?

The third pressing decision is: In December 2025, how aggressively should governments and industry push AI data‑center build‑out, and under what efficiency and sustainability constraints? This is no longer a distant environmental question; it has become a near‑term bottleneck for AI growth and a live political issue around grid capacity, water use, and local community impacts.mccormick.senate+2​

Several late‑2025 reports and bills put hard numbers on the problem. Lawmakers cite analyses from the Lawrence Berkeley National Laboratory showing that data centers’ share of US electricity consumption has more than doubled in recent years and could potentially triple again by 2028, with AI being a primary driver. A November 19, 2025 press release from Senators Dave McCormick and Chris Coons describes AI data center energy demand rising toward hundreds of terawatt‑hours—comparable to the electricity used by major portions of US residential activity—if current trends continue. Articles covering these moves emphasize that this kind of growth strains utilities, raises bills for ordinary consumers, and complicates climate commitments.fedscoop+2​

In response, US lawmakers have introduced the “Liquid Cooling for AI Act of 2025,” which would push more efficient cooling technologies and heat‑reuse systems, and commission detailed federal studies on where and how to deploy liquid cooling. November 24 and 25 coverage explains that liquid cooling can dramatically reduce water use and allow more of a data center’s energy budget to go to compute rather than air‑cooling overhead, potentially avoiding expensive grid upgrades. The Trump administration’s AI action planning, referenced in some of this reporting, also points toward making federal lands available for data centers and associated power infrastructure, raising questions about environmental reviews and local community input.meritalk+2​

This is not just a US issue. Globally, AI data centers are emerging as one of the fastest‑growing loads on electricity grids in Europe, East Asia, and the Middle East, and regions differ in how much surplus clean energy they have. The EU’s digital and climate strategies stress that AI development must stay compatible with emissions goals, which implies tough decisions on where to permit new facilities and under what energy‑mix conditions. Countries with abundant hydropower, nuclear capacity, or strong renewables may lean into AI hosting as a new export sector, while others worry about locking in more fossil generation to serve AI workloads.digital-strategy.europa+2​

The decision is critical because compute is the limiting reagent for the next generation of AI models. If governments green‑light an all‑out build‑out without strict efficiency and sustainability rules, the world may end up with:

• Severe local impacts: higher electricity prices, water stress in already‑dry regions, and conflicts with residential or agricultural users.mccormick.senate+2​
• Climate setbacks: increased emissions if the marginal electricity comes from fossil sources.
• Political backlash: communities and regulators turning against AI infrastructure once costs hit households.

If, on the other hand, they impose tight constraints—such as requiring high power‑usage effectiveness (PUE) thresholds, strong heat‑reuse standards, and clean‑energy procurement for new AI data centers—then AI growth might slow or shift geographically but become more sustainable and politically stable. The Liquid Cooling for AI Act hints at a pathway where technical standards and best practices (liquid cooling, heat reuse, grid‑friendly siting) are developed in partnership between industry and government, and then used as a soft‑law template for regulation or procurement across agencies.fedscoop+2​

Key organizations in this decision include:

• US federal actors: Senators McCormick and Coons; the Department of Energy; the Government Accountability Office; and the Trump White House shaping broader AI infrastructure policy.meritalk+2​
• Major AI/cloud companies: Microsoft, Google, Amazon, Meta, Oracle, and others building hyperscale AI data centers, plus chip makers like Nvidia and AMD whose hardware efficiency strongly affects energy demand.supplychaindigital+4​
• International bodies and national regulators: EU energy and climate directorates, national environment agencies, and regional grid operators that approve new data‑center projects and set climate‑compatibility rules.anecdotes+1​

Furthermore, this infrastructure decision is intertwined with geopolitics and export controls. US policy on high‑end Nvidia GPUs like the H200 and the handling of AI chip exports to China directly affects where compute capacity gets built and who has access. Late‑November 2025 reporting indicates that the Trump administration is considering loosening some chip export rules, while US lawmakers simultaneously push to close perceived loopholes and bar advanced chips destined for China. If advanced chips remain tightly constrained, US‑aligned countries could consolidate AI compute leadership, but may also face higher hardware costs and slower scale‑out; if controls loosen, global compute growth could accelerate but at the risk of empowering strategic competitors and intensifying energy and climate pressures worldwide.cnn+5​

December 2025 choices—on bills like the Liquid Cooling for AI Act, on permitting rules for new AI campuses, and on chip export policy—will strongly influence the shape of AI infrastructure through the late 2020s. A relatively coordinated approach could push the industry into an efficiency race (better cooling, more efficient chips, more strategic siting), while an uncoordinated one might lead to a brute‑force build‑out, emergent grid crises, and a political backlash that eventually forces harsher restrictions. Given how central compute is to everything else in AI—from safety research to open‑source viability—this infrastructure decision is easily one of the three most important on the table for December.reuters+4​

For full bibliographic details on this topic, see: FedScoop’s report “Lawmakers eye liquid cooling tech to solve AI data center problems” (FedScoop, Nov. 24, 2025, https://fedscoop.com/liquid-cool-technology-ai-data-centers-senate-house-bill); MerITalk’s coverage “Senators Back Liquid Cooling for AI Data Centers, to Curb Water Usage and Costs” (MerITalk, Nov. 25, 2025, https://meritalk.com/articles/senators-back-liquid-cooling-for-ai-data-centers-to-curb-water-usage-and-costs); and the official press release “Senators McCormick, Coons Introduce Bill to Boost U.S. AI Leadership with Energy-Efficient Data Center Cooling” (Office of Senator Dave McCormick, Nov. 20, 2025, https://www.mccormick.senate.gov/press-releases/senators-mccormick-coons-introduce-bill-to-boost-u-s-ai-leadership-with-energy-efficient-data-center-cooling).[3]fedscoop+1​

Sources

1. https://www.anecdotes.ai/learn/ai-regulations-in-2025-us-eu-uk-japan-china-and-more
2. https://www.clarifai.com/blog/kimi-k2-vs-deepseek-v3/r1
3. https://www.mccormick.senate.gov/press-releases/senators-mccormick-coons-introduce-bill-to-boost-u-s-ai-leadership-with-energy-efficient-liquid-cooling-technology/
4. https://www.goodwinlaw.com/en/insights/publications/2025/11/alerts-technology-aiml-california-moves-to-regulate-frontier-ai-with-a-focus-on-catastrophic-risk
5. https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai
6. https://www.gov.ca.gov/2025/09/29/governor-newsom-signs-sb-53-advancing-californias-world-leading-artificial-intelligence-industry/
7. https://www.globalpolicywatch.com/2025/11/white-house-drafts-executive-order-to-preempt-state-ai-laws/
8. https://www.nasdaq.com/articles/us-weighs-allowing-nvidia-sell-advanced-h200-ai-chips-china
9. https://www.shakudo.io/blog/top-9-large-language-models
10. https://news.smol.ai/issues/25-07-11-kimi-k2/
11. https://internationalaisafetyreport.org
12. https://www.ibm.com/think/news/open-source-models-surpass-closed-models
13. https://www.aclu.org/news/privacy-technology/open-source-llms
14. https://fedscoop.com/liquid-cool-technology-ai-data-centers-senate-house-bill/
15. https://meritalk.com/articles/senators-back-liquid-cooling-for-ai-data-centers-to-curb-water-usage-and-costs/
16. https://supplychaindigital.com/news/trump-barred-china-nvidias-blackwell-ai-chips
17. https://www.cnn.com/2025/08/11/china/us-china-trade-nvidia-chips-intl-hnk
18. https://www.reuters.com/world/asia-pacific/us-considering-letting-nvidia-sell-h200-chips-china-sources-say-2025-11-21/
19. https://www.newsweek.com/gop-rep-the-u-s-must-close-critical-ai-chip-export-loophole-exploited-by-china-opinion-11102618
20. https://www.theinformation.com/articles/china-slowly-surely-breaking-free-nvidia
21. https://www.regulatoryoversight.com/2025/10/california-charts-the-frontier-with-first-law-setting-reporting-and-compliance-requirements-for-powerful-frontier-ai-models/
22. https://www.reddit.com/r/singularity/comments/1lx9ped/kimi_k2_new_sota_nonreasoning_model_1t_parameters/
23. https://www.gov.uk/government/publications/ai-safety-summit-2023-the-bletchley-declaration/the-bletchley-declaration-by-countries-attending-the-ai-safety-summit-1-2-november-2023

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